Simulating noisy variational quantum eigensolver with local noise models

• URL: http://arxiv.org/abs/2010.14821v2
• Date: Wed, 14 Apr 2021 06:54:23 GMT
• Title: Simulating noisy variational quantum eigensolver with local noise models
• Authors: Jinfeng Zeng, Zipeng Wu, Chenfeng Cao, Chao Zhang, Shiyao Hou, Pengxiang Xu, Bei Zeng
• Abstract summary: Variational quantum eigensolver (VQE) is promising to show quantum advantage on near-term noisy-intermediate-scale quantum computers. One central problem of VQE is the effect of noise, especially the physical noise on realistic quantum computers. We study systematically the effect of noise for the VQE algorithm, by performing numerical simulations with various local noise models.
• Score: 4.581041382009666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Variational quantum eigensolver (VQE) is promising to show quantum advantage on near-term noisy-intermediate-scale quantum (NISQ) computers. One central problem of VQE is the effect of noise, especially the physical noise on realistic quantum computers. We study systematically the effect of noise for the VQE algorithm, by performing numerical simulations with various local noise models, including the amplitude damping, dephasing, and depolarizing noise. We show that the ground state energy will deviate from the exact value as the noise probability increase and normally noise will accumulate as the circuit depth increase. We build a noise model to capture the noise in a real quantum computer. Our numerical simulation is consistent with the quantum experiment results on IBM Quantum computers through Cloud. Our work sheds new light on the practical research of noisy VQE. The deep understanding of the noise effect of VQE may help to develop quantum error mitigation techniques on near team quantum computers.

Related papers

• Enhancing Quantum Variational Algorithms with Zero Noise Extrapolation via Neural Networks [0.4779196219827508]
  Variational Quantum Eigensolver (VQE) is a promising algorithm for solving complex quantum problems. The ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes. This research introduces a novel approach by utilizing neural networks for zero noise extrapolation (ZNE) in VQE computations.
  arXiv  Detail & Related papers  (2024-03-10T15:35:41Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Variational Denoising for Variational Quantum Eigensolver [0.28675177318965045]
  The variational quantum eigensolver (VQE) is a hybrid algorithm that has the potential to provide a quantum advantage in practical chemistry problems. VQE faces challenges in task-specific design and machine-specific architecture, particularly when running on noisy quantum devices. We propose variational denoising, an unsupervised learning method that employs a parameterized quantum neural network to improve the solution of VQE.
  arXiv  Detail & Related papers  (2023-04-02T14:56:15Z)
• Noise-assisted digital quantum simulation of open systems [1.3124513975412255]
  We present a novel approach that capitalizes on the intrinsic noise of quantum devices to reduce the computational resources required for simulating open quantum systems. Specifically, we selectively enhance or reduce decoherence rates in the quantum circuit to achieve the desired simulation of open system dynamics.
  arXiv  Detail & Related papers  (2023-02-28T14:21:43Z)
• Noise-robust ground state energy estimates from deep quantum circuits [0.0]
  We show how the underlying energy estimate explicitly filters out incoherent noise in quantum algorithms. We implement QCM for a model of quantum magnetism on IBM Quantum hardware. We find that QCM maintains a remarkably high degree of error robustness where VQE completely fails.
  arXiv  Detail & Related papers  (2022-11-16T09:12:55Z)
• QuEst: Graph Transformer for Quantum Circuit Reliability Estimation [32.89844497610906]
  Python library called TorchQuantum can construct, simulate, and train PQC for machine learning tasks. We propose to leverage a graph transformer model to predict noise impact on circuit fidelity. Compared with circuit simulators, the predictor has over 200X speedup for estimating the fidelity.
  arXiv  Detail & Related papers  (2022-10-30T02:35:31Z)
• Iterative Qubits Management for Quantum Index Searching in a Hybrid System [56.39703478198019]
  IQuCS aims at index searching and counting in a quantum-classical hybrid system. We implement IQuCS with Qiskit and conduct intensive experiments. Results demonstrate that it reduces qubits consumption by up to 66.2%.
  arXiv  Detail & Related papers  (2022-09-22T21:54:28Z)
• Simulating strongly interacting Hubbard chains with the Variational Hamiltonian Ansatz on a quantum computer [0.0]
  Variational Quantum Eigensolver (VQE) has been implemented to study molecules and condensed matter systems on small size quantum computers. We try to answer the question: how much of the underlying physics of a 1D Hubbard chain is described by a problem-inspired Variational Hamiltonian Ansatz (VHA) in a broad range of parameter values. Our findings suggest that even low fidelity solutions capture energy and number of doubly occupied sites well, while spin-spin correlations are not well captured even when the solution is of high fidelity.
  arXiv  Detail & Related papers  (2021-11-23T16:54:36Z)
• Pulse-level noisy quantum circuits with QuTiP [53.356579534933765]
  We introduce new tools in qutip-qip, QuTiP's quantum information processing package. These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features. We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian.
  arXiv  Detail & Related papers  (2021-05-20T17:06:52Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.